# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

from dataclasses import dataclass, field
from typing import Optional

import torch
from omegaconf import DictConfig
from omegaconf import MISSING
from torch.optim import Optimizer

from openspeech.dataclass.configurations import LearningRateSchedulerConfigs
from openspeech.optim.scheduler import register_scheduler
from openspeech.optim.scheduler.lr_scheduler import LearningRateScheduler


@dataclass
class WarmupLRSchedulerConfigs(LearningRateSchedulerConfigs):
    scheduler_name: str = field(default="warmup", metadata={"help": "Name of learning rate scheduler."})
    # peak_lr: float = field(default=1e-04, metadata={"help": "Maximum learning rate."})
    peak_lr: float = field(default=MISSING, metadata={"help": "Maximum learning rate."})
    init_lr: float = field(default=1e-7, metadata={"help": "Initial learning rate."})
    warmup_steps: int = field(
        # default=4000, metadata={"help": "Warmup the learning rate linearly for the first N updates"}
        default=MISSING, metadata={"help": "Warmup the learning rate linearly for the first N updates"}
    )
    total_steps: int = field(default=200000, metadata={"help": "Total training steps."})


@register_scheduler("warmup", dataclass=WarmupLRSchedulerConfigs)
class WarmupLRScheduler(LearningRateScheduler):
    """
    Warmup learning rate until `total_steps`

    Args:
        optimizer (Optimizer): wrapped optimizer.
        configs (DictConfig): configuration set.
    """

    def __init__(
        self,
        optimizer: Optimizer,
        configs: DictConfig,
    ) -> None:
        super(WarmupLRScheduler, self).__init__(optimizer, configs.lr_scheduler.init_lr)
        if configs.lr_scheduler.warmup_steps != 0:
            warmup_rate = configs.lr_scheduler.peak_lr - configs.lr_scheduler.init_lr
            self.warmup_rate = warmup_rate / configs.lr_scheduler.warmup_steps
        else:
            self.warmup_rate = 0
        self.update_steps = 1
        self.lr = configs.lr_scheduler.init_lr
        self.warmup_steps = configs.lr_scheduler.warmup_steps

    def step(self, val_loss: Optional[torch.FloatTensor] = None):
        if self.update_steps < self.warmup_steps:
            lr = self.init_lr + self.warmup_rate * self.update_steps
            self.set_lr(self.optimizer, lr)
            self.lr = lr
        self.update_steps += 1
        return self.lr
